class name changed, documents modified

4 files changed, 18 insertions, 12 deletions

diff --git a/src/python/doc/rips_complex_ref.rst b/src/python/doc/rips_complex_ref.rst
index f781fd92..2aa6b268 100644
--- a/src/python/doc/rips_complex_ref.rst
+++ b/src/python/doc/rips_complex_ref.rst
@@ -32,9 +32,9 @@ Weighted Rips complex reference manual
 DTM Rips complex reference manual
 =================================
 
-.. autoclass:: gudhi.dtm_rips_complex.DtmRipsComplex
+.. autoclass:: gudhi.dtm_rips_complex.DTMRipsComplex
    :members:
    :undoc-members:
    :show-inheritance:
 
-   .. automethod:: gudhi.dtm_rips_complex.DtmRipsComplex.__init__
\ No newline at end of file
+   .. automethod:: gudhi.dtm_rips_complex.DTMRipsComplex.__init__
\ No newline at end of file
diff --git a/src/python/doc/rips_complex_user.rst b/src/python/doc/rips_complex_user.rst
index ac11a4b6..450e6c1a 100644
--- a/src/python/doc/rips_complex_user.rst
+++ b/src/python/doc/rips_complex_user.rst
@@ -378,6 +378,7 @@ Example from a point cloud combined with DistanceToMeasure
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Combining with DistanceToMeasure, one can compute the DTM-filtration of a point set, as in `this notebook <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-DTM-filtrations.ipynb>`_. 
+Remark that DTMRipsComplex class provides exactly this function.
 
 .. testcode::
 
@@ -402,14 +403,15 @@ The output is:
 DTM Rips Complex
 ----------------
 
-`DtmdRipsComplex <rips_complex_ref.html#dtm-rips-complex-reference-manual>`_ builds a simplicial complex from a point set or a full distence matrix (in the form of ndarray), as described in the above example.
+`DTMRipsComplex <rips_complex_ref.html#dtm-rips-complex-reference-manual>`_ builds a simplicial complex from a point set or a full distence matrix (in the form of ndarray), as described in the above example.
+This class constructs a weighted Rips complex giving larger weights to outliers, which reduces their impact on the persistence diagram. See `this notebook <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-DTM-filtrations.ipynb>`_ for some experiments.
 
 .. testcode::
 
     import numpy as np
-    from gudhi.dtm_rips_complex import DtmRipsComplex
+    from gudhi.dtm_rips_complex import DTMRipsComplex
     pts = np.array([[2.0, 2.0], [0.0, 1.0], [3.0, 4.0]])
-    dtm_rips = DtmRipsComplex(points=pts, k=2)
+    dtm_rips = DTMRipsComplex(points=pts, k=2)
     st = dtm_rips.create_simplex_tree(max_dimension=2)
     print(st.persistence())
 
diff --git a/src/python/gudhi/dtm_rips_complex.py b/src/python/gudhi/dtm_rips_complex.py
index 6d2f9f31..70c8e5dd 100644
--- a/src/python/gudhi/dtm_rips_complex.py
+++ b/src/python/gudhi/dtm_rips_complex.py
@@ -12,7 +12,7 @@ from gudhi.weighted_rips_complex import WeightedRipsComplex
 from gudhi.point_cloud.dtm import DistanceToMeasure
 from scipy.spatial.distance import cdist
 
-class DtmRipsComplex(WeightedRipsComplex):
+class DTMRipsComplex(WeightedRipsComplex):
     """
     Class to generate a DTM Rips complex from a distance matrix or a point set, 
     in the way described in :cite:`dtmfiltrations`.
@@ -28,7 +28,7 @@ class DtmRipsComplex(WeightedRipsComplex):
         """
         Args:
             points (Sequence[Sequence[float]]): list of points.
-            distance_matrix (ndarray): full distance matrix.
+            distance_matrix (numpy.ndarray): full distance matrix.
             k (int): number of neighbors for the computation of DTM. Defaults to 1, which is equivalent to the usual Rips complex.
             q (float): order used to compute the distance to measure. Defaults to 2.
             max_filtration (float): specifies the maximal filtration value to be considered.      
@@ -39,8 +39,12 @@ class DtmRipsComplex(WeightedRipsComplex):
                 points=[]
             distance_matrix = cdist(points,points)
         self.distance_matrix = distance_matrix
-        dtm = DistanceToMeasure(k, q=q, metric="precomputed")
+
         # TODO: address the error when k is too large 
-        self.weights = dtm.fit_transform(distance_matrix)
+        if k <= 1:
+            self.weights = [0] * len(distance_matrix)
+        else:
+            dtm = DistanceToMeasure(k, q=q, metric="precomputed")        
+            self.weights = dtm.fit_transform(distance_matrix)
         self.max_filtration = max_filtration
         
diff --git a/src/python/test/test_dtm_rips_complex.py b/src/python/test/test_dtm_rips_complex.py
index bc6e5a59..7cd2ad90 100644
--- a/src/python/test/test_dtm_rips_complex.py
+++ b/src/python/test/test_dtm_rips_complex.py
@@ -8,14 +8,14 @@
       - YYYY/MM Author: Description of the modification
 """
 
-from gudhi.dtm_rips_complex import DtmRipsComplex
+from gudhi.dtm_rips_complex import DTMRipsComplex
 import numpy as np
 from math import sqrt
 import pytest
 
 def test_dtm_rips_complex():
     pts = np.array([[2.0, 2.0], [0.0, 1.0], [3.0, 4.0]])
-    dtm_rips = DtmRipsComplex(points=pts, k=2)
+    dtm_rips = DTMRipsComplex(points=pts, k=2)
     st = dtm_rips.create_simplex_tree(max_dimension=2)
     st.persistence()
     persistence_intervals0 = st.persistence_intervals_in_dimension(0)
@@ -23,7 +23,7 @@ def test_dtm_rips_complex():
     
 def test_compatibility_with_rips():
     distance_matrix = np.array([[0, 1, 1, sqrt(2)], [1, 0, sqrt(2), 1], [1, sqrt(2), 0, 1], [sqrt(2), 1, 1, 0]])
-    dtm_rips = DtmRipsComplex(distance_matrix=distance_matrix, max_filtration=42)
+    dtm_rips = DTMRipsComplex(distance_matrix=distance_matrix, max_filtration=42)
     st = dtm_rips.create_simplex_tree(max_dimension=1)
     assert list(st.get_filtration()) == [
         ([0], 0.0),